The impact of 14th Malaysian General Election on Bursa Malaysia by using a complex network approach

Authors

  • Hafizah Bahaludin Department of Computational and Theoretical Sciences, Kulliyyah of Science, International Islamic University Malaysia, Kuantan, Pahang, 25200, Malaysia Author
  • Fatin Nur Amirah Mahamood Department of Computational and Theoretical Sciences, Kulliyyah of Science, International Islamic University Malaysia, Kuantan, Pahang, 25200, Malaysia. Author
  • Muhammad Hasanuddin Amran Department of Computational and Theoretical Sciences, Kulliyyah of Science, International Islamic University Malaysia, Kuantan, Pahang, 25200, Malaysia. Author

DOI:

https://doi.org/10.61841/dyfa9d57

Keywords:

Network, Bursa Malaysia, Minimal Spanning Tree, Centrality Measures

Abstract

A complex system such as a financial market can be visualized in the form of a network. For instance, a network is used to exhibits the interconnection between stocks that are traded in the market. In addition, it shows many important embedded information in a network such as structural changes that are affected from any events. Even though, the applications of a complex network has been used to investigate the behaviour of Bursa Malaysia, far too little attention has been paid to the effect of political event towards Malaysian financial market. Thus, this study concentrates the effect of 14th Malaysian General Election towards Bursa Malaysia. The first objective is to construct a financial network of Bursa Malaysia and the second objective is to examine the importance of the stocks on the financial network. The data used are the shariah-compliant stocks listed on Shariah Advisory Council (SAC). The duration of the study is divided into two periods which are the six months before and after the general election. The minimal spanning tree is used to construct a network and centrality measures are used to examine the role of a stock in a network. The empirical findings revealed and shed light on the impact of the 14th Malaysian General Election on shariah-compliant securities.

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References

1. Aswani, J. (2017). Impact of global financial crisis on network of Asian stock markets. Algorithmic Finance, 6, 79–91. https://doi.org/10.3233/AF-170192

2. Bahaludin, H., Abdullah, M. H., Siew, L. W., & Hoe, L. W. (2019). The Investigation on the Impact of Financial Crisis on Bursa Malaysia Using Minimal Spanning Tree. Mathematics and Statistics, 7(4A), 1–

8. https://doi.org/10.13189/ms.2019.070701

3. Bonacich, P. (1987). Power and Centrality : A Family of Measures. American Journal of Sociology, 92(5), 1170–1182.

4. Coletti, P., & Murgia, M. (2016). The Network of the Italian Stock Market during the 2008-2011 Financial Crises. Algorithmic Finance, 5(3–4), 111–137. https://doi.org/10.3233/AF-160177

5. Djauhari, M. A., & Gan, S. L. (2013). Minimal spanning tree problem in stock networks analysis: An efficient algorithm. Physica A: Statistical Mechanics and Its Applications, 392(9), 2226–2234. https://doi.org/10.1016/j.physa.2012.12.032

6. Djauhari, M. A., & Gan, S. L. (2014a). Bursa Malaysia Stocks Market Analysis : A Review. ASM Science Journal, 8(2), 150–158.

7. Djauhari, M. A., & Gan, S. L. (2014b). Optimality problem of network topology in stocks market analysis. Physica A: Statistical Mechanics and Its Applications, 419(2015), 108–114. https://doi.org/10.1016/j.physa.2014.09.060

8. Freeman, L. C. (1977). A Set of Measures of Centrality Based on Betweenness. Sociometry, 40(1), 35. https://doi.org/10.2307/3033543

9. Freeman, L. C. (1978). Centrality in Social Networks Conceptual Clarification. Social Networks, 1(1968), 215–239.

10. Gan, S. L., & Djauhari, M. A. (2012). Stock Network Analysis in Kuala Lumpur Stock Exchange. Malaysian Journal of Fundamental & Applied Sciences, 8(2), 60–66.

11. Jolliffe, I. T. (2002). Principal Component Analysis. Springer Series in Statistics (Second Edi, Vol. 98). Springer. https://doi.org/10.1007/b98835

12. Kruskal, J. (1956). On the Shortest Spanning Subtree of a Graph and the Traveling Salesman Problem. In

Proceedings of the American Mathematical Society (pp. 48–50). American Mathemtical Society.

13. Lee, J. W., & Nobi, A. (2018). State and Network Structures of Stock Markets Around the Global Financial Crisis. Computational Economics, 51(2), 195–210. https://doi.org/10.1007/s10614-017-9672-x

14. Li, Y., Jiang, X., Tian, Y., Li, S., & Zheng, B. (2018). Portfolio optimization based on network topology. Physica A. https://doi.org/10.1016/j.physa.2018.10.014

15. Lim, S. Y., Salleh, R. M., & Asrah, N. M. (2018). Multidimensional Minimal Spanning Tree : The Bursa Malaysia. Journal of Science and Technology, 10(2), 136–143.

16. Mahamood, F. N. A., Bahaludin, H., & Abdullah, M. H. (2019). A Network Analysis of Shariah-Compliant Stocks across Global Financial Crisis : A Case of Malaysia. Modern Applied Science, 13(7), 81–93. https://doi.org/10.5539/mas.v13n7p80

17. Majapa, M., & Gossel, S. J. (2016). Topology of the South African stock market network across the 2008 financial crisis. Physica A, 445, 35–47. https://doi.org/10.1016/j.physa.2015.10.108

18. Malkevitch, J. (2012). Trees : A Mathematical Tool for All Seasons. American Mathematical Society, Feature Column, 1–16.

19. Mantegna, R. N. (1999). Hierarchical structure in financial markets. European Physical Journal B, 11(1), 193–197. https://doi.org/10.1007/s100510050929

20. Memon, B. A., & Yao, H. (2019). Structural Change and Dynamics of Pakistan Stock Market During Crisis: A Complex Network Perspective. Entropy, 21(3), 248. https://doi.org/10.3390/e21030248

21. Memon, B. A., Yao, H., & Tahir, R. (2020). General election effect on the network topology of Pakistan ’ s stock market : network-based study of a political event. Financial Innovation, 6(2), 1–14.

22. Nesetril, J. (1997). A Few Remarks on the History of MST-Problem. Archivum Mathematicum, 33(1–2), 15–22.

23. Pasini, G. (2017). Principal Component Analysis for Stock Portfolio Management. International Journal of Pure and Apllied Mathematics, 115(1), 153–167. https://doi.org/10.12732/ijpam.v115i1.12

24. Peralta, G., & Zareei, A. (2016). A Network Approach to Portfolio Selection. Journal of Empirical Finance, 38, 157–180.

25. Pozzi, F., Di Matteo, T., & Aste, T. (2013). Spread of risk across financial markets: Better to invest in the peripheries. Scientific Reports, 3, 1–7. https://doi.org/10.1038/srep01665

26. Sharif, S., & Djauhari, M. A. (2012). A Proposed Centrality Measure: The Case of Stocks Traded at Bursa Malaysia. Modern Applied Science, 6(10), p62. https://doi.org/10.5539/mas.v6n10p62

27. Tabak, B. M., Serra, T. R., & Cajueiro, D. O. (2010). Topological properties of stock market networks : The case of Brazil. Physica A, 389(16), 3240–3249. https://doi.org/10.1016/j.physa.2010.04.002

28. Yao, H., & Memon, B. A. (2019). Network topology of FTSE 100 Index companies: From the perspective of Brexit. Physica A: Statistical Mechanics and Its Applications, 523, 1248–1262. https://doi.org/10.1016/j.physa.2019.04.106

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Published

31.10.2020

How to Cite

Bahaludin, H., Mahamood, F. N. A., & Hasanuddin Amran, M. (2020). The impact of 14th Malaysian General Election on Bursa Malaysia by using a complex network approach. International Journal of Psychosocial Rehabilitation, 24(8), 12073-12083. https://doi.org/10.61841/dyfa9d57